On syntactic anonymity and differential privacy

Chris Clifton, Tamir Tassa

Research output: Contribution to journalArticlepeer-review

66 Scopus citations

Abstract

Recently, there has been a growing debate over approaches for handling and analyzing private data. Research has identified issues with syntactic approaches such as k-anonymity and ℓ-diversity. Differential privacy, which is based on adding noise to the analysis outcome, has been promoted as the answer to privacy-preserving data mining. This paper looks at the issues involved and criticisms of both approaches. We conclude that both approaches have their place, and that each approach has issues that call for further research. We identify these research challenges, and discuss recent developments and future directions that will enable greater access to data while improving privacy guarantees.

Original languageEnglish
Pages (from-to)161-183
Number of pages23
JournalTransactions on Data Privacy
Volume6
Issue number2
StatePublished - 22 Aug 2013
Externally publishedYes

ASJC Scopus subject areas

  • Software
  • Statistics and Probability

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